library(tidyverse)
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library(scales)

Attaching package: ‘scales’

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library(plotly)
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library(lubridate)

Attaching package: ‘lubridate’

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library(sf)
Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUE
library(zoo)

Attaching package: ‘zoo’

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    as.Date, as.Date.numeric

Covid Tab

#load in beds and add year column
beds <- read_csv("raw_data/non_covid_raw_data/beds_by_nhs_board_of_treatment_and_specialty.csv") %>% janitor::clean_names()
Warning: One or more parsing issues, see `problems()` for detailsRows: 30458 Columns: 20── Column specification ─────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (10): Quarter, QuarterQF, HB, HBQF, Location, LocationQF, Specialty, SpecialtyQF, Special...
dbl  (5): AllStaffedBeddays, TotalOccupiedBeddays, AverageAvailableStaffedBeds, AverageOccupi...
lgl  (5): AllStaffedBeddaysQF, TotalOccupiedBeddaysQF, AverageAvailableStaffedBedsQF, Average...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
beds <- beds %>% 
mutate(quarter_date = yq(quarter),
       year = year(quarter_date), 
       .after = quarter)

beds %>% 
write_csv("clean_data/non_covid_data/beds.csv")
# bed percentage availablity for "all acute"
# Will need to add filter for year based on user input
beds_plotly <- beds %>%
  filter(specialty_name == "All Acute") %>% 
  group_by(quarter, specialty_name) %>%
  summarise(mean_perc_occ = mean(percentage_occupancy)) %>% 
  ggplot(aes(x = quarter, y = mean_perc_occ))+
  geom_line(aes(colour = specialty_name, group = specialty_name))+
  geom_point(aes(
    text = paste0("Occupancy:",round(mean_perc_occ, digits = 2)," %\n", quarter)),
    size = 0.5)+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  labs(title = "Mean bed availability for all Acute Patients",
       x = "\nYear and Quarter",
       y = "Average Percentage Occupancy")
`summarise()` has grouped output by 'quarter'. You can override using the `.groups` argument.Warning: Ignoring unknown aesthetics: text
ggplotly(beds_plotly, tooltip = "text") %>% 
  config(displayModeBar = FALSE) %>% 
  layout(hoverlabel=list(bgcolor="white"))
NA
NA

Scotland Shapefile

ae_wait_times wrangling

ae_wait_times <- read_csv("raw_data/non_covid_raw_data/monthly_ae_waitingtimes_202206.csv") %>% janitor::clean_names()
Rows: 15837 Columns: 25── Column specification ─────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (13): Country, HBT, TreatmentLocation, DepartmentType, NumberOfAttendancesEpisodeQF, Numb...
dbl (12): Month, NumberOfAttendancesAggregate, NumberOfAttendancesEpisode, NumberMeetingTarge...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#make a date and year column with the first date of every month
ae_wait_times <- ae_wait_times %>% 
  mutate(date = ym(month), .after = month,
         year = year(date))

#make a percent column with percent of patients meeting the 4hr target time
ae_wait_times <- ae_wait_times %>% 
  mutate(percent_4hr_target_achieved = (number_meeting_target_aggregate/number_of_attendances_aggregate)*100)

ae_wait_times %>% 
write_csv("clean_data/non_covid_data/ae_wait_times.csv")
#write in the target data for the shapefile colours
target_2007 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2007) %>% 
  rename(ae_target_2007 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2007)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
  

target_2008 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>%
  filter(year == 2008) %>% 
  rename(ae_target_2008 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2008)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2009 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2009) %>% 
  rename(ae_target_2009 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2009)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2010 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2010) %>% 
  rename(ae_target_2010 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2010)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2011 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2011) %>% 
  rename(ae_target_2011 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2011)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2012 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2012) %>% 
  rename(ae_target_2012 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2012)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2013 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2013) %>% 
  rename(ae_target_2013 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2013)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2014 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved =
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2014) %>% 
  rename(ae_target_2014 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2014)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2015 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2015) %>% 
  rename(ae_target_2015 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2015)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2016 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2016) %>% 
  rename(ae_target_2016 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2016)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2017 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2017) %>% 
  rename(ae_target_2017 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2017)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2018 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2018) %>% 
  rename(ae_target_2018 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2018)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2019 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2019) %>% 
  rename(ae_target_2019 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2019)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2020 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2020) %>% 
  rename(ae_target_2020 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2020)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
target_2021 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2021) %>% 
  rename(ae_target_2021 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2021)
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
  

shape file wrangling

#scotland_smaller$Shape_Area <- as.character(scotland_smaller$Shape_Area)
st_write(scotland_smaller, "clean_data/shapefile/scotland_smaller.gpkg", append = FALSE) 
Warning: Dropping column(s) centres of class(es) sfc_POINT;sfc
Deleting layer `scotland_smaller' using driver `GPKG'
Writing layer `scotland_smaller' to data source 
  `clean_data/shapefile/scotland_smaller.gpkg' using driver `GPKG'
Writing 14 features with 21 fields and geometry type Multi Polygon.

SIMD graph

simd <- read_csv("raw_data/non_covid_raw_data/inpatient_and_daycase_by_nhs_board_of_treatment_and_simd.csv") %>% janitor::clean_names()
Rows: 40821 Columns: 18── Column specification ─────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (11): Quarter, QuarterQF, HB, HBQF, Location, LocationQF, AdmissionType, AdmissionTypeQF,...
dbl  (7): SIMD, Episodes, LengthOfEpisode, AverageLengthOfEpisode, Stays, LengthOfStay, Avera...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
simd <- simd %>% 
mutate(quarter_date = yq(quarter),
       year = year(quarter_date), 
       .after = quarter)

simd %>% 
write_csv("clean_data/non_covid_data/simd.csv")
# average episodes by SIMD value
# currently unfiltered for health board or admission type etc
simd_plotly <- simd %>% 
  drop_na(simd) %>%
  mutate(simd = as.factor(simd)) %>% # gives each simd a separate colour
  group_by(quarter, simd) %>% 
  summarise(avg_episodes = mean(episodes, na.rm = TRUE)) %>% 
  ggplot(aes(x = quarter, y = avg_episodes, group = simd))+
  geom_line(aes(colour = simd))+
  geom_point(size = 0.5)+
  scale_y_continuous(labels = scales::comma)+
  labs(title = "Average Hospital Episodes by SIMD Deprevation score\n",
       x = "\nYear and Quarter",
       y = "Average Episodes\n")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
`summarise()` has grouped output by 'quarter'. You can override using the `.groups` argument.
ggplotly(simd_plotly) %>% 
  config(displayModeBar = FALSE)

Age Graph

age_sex <- read_csv("raw_data/non_covid_raw_data/inpatient_and_daycase_by_nhs_board_of_treatment_age_and_sex.csv") %>% janitor::clean_names()
Rows: 129393 Columns: 18── Column specification ─────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (12): Quarter, QuarterQF, HB, HBQF, Location, LocationQF, AdmissionType, AdmissionTypeQF,...
dbl  (6): Episodes, LengthOfEpisode, AverageLengthOfEpisode, Stays, LengthOfStay, AverageLeng...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
age_sex <- age_sex %>%
    mutate(quarter_date = yq(quarter),
           year = year(quarter_date),
           .after = quarter)

age_sex %>% 
  write_csv("clean_data/non_covid_data/age_sex.csv")

# Average number of episode for age groups
# currently unfiltered by department or anything else
age_plotly <- age_sex %>% 
  #filter(min_date < year & year < max_date) %>% 
  group_by(quarter, age) %>% 
  summarise(avg_episodes = mean(episodes, na.rm = TRUE)) %>% 
  ggplot(aes(x = quarter, y = avg_episodes))+
  geom_line(aes(colour = age, group = age))+ 
  geom_point(size = 0.5)+
  labs(title = "Average Hospital Episodes by Age Groups\n",
       x = "\nYear and Quarter",
       y = "Average Episodes\n")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
`summarise()` has grouped output by 'quarter'. You can override using the `.groups` argument.
  
ggplotly(age_plotly) %>% 
  config(displayModeBar = FALSE)

Gender

sex_plotly <- age_sex %>% 
  group_by(quarter, sex) %>% 
  summarise(avg_length_of_episode = mean(average_length_of_episode, na.rm = TRUE)) %>% 
  ggplot(aes(x = quarter, y = avg_length_of_episode))+
  geom_line(aes(colour = sex, group = sex))+
  #geom_smooth(aes(colour = sex, group = sex), se = FALSE)+
  geom_point(size = 0.5)+
  labs(title = "Average Hospital Episodes by Gender\n",
       x = "\nYear and Quarter",
       y = "Average Episodes\n")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
`summarise()` has grouped output by 'quarter'. You can override using the `.groups` argument.
  

ggplotly(sex_plotly, hovertemplate = ) %>% 
  config(displayModeBar = FALSE)

A&E Tab

ae_wait_times <- read_csv("raw_data/non_covid_raw_data/monthly_ae_waitingtimes_202206.csv") %>% janitor::clean_names()
Rows: 15837 Columns: 25── Column specification ───────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (13): Country, HBT, TreatmentLocation, DepartmentType, NumberOfAttendancesEpisodeQF, Nu...
dbl (12): Month, NumberOfAttendancesAggregate, NumberOfAttendancesEpisode, NumberMeetingTar...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#make a date and year column with the first date of every month
ae_wait_times <- ae_wait_times %>% 
  mutate(date = ym(month), .after = month,
         year = year(date))

#make a percent column with percent of patients meeting the 4hr target time
ae_wait_times <- ae_wait_times %>% 
  mutate(percent_4hr_target_achieved = (number_meeting_target_aggregate/number_of_attendances_aggregate)*100)

ae_wait_times %>% 
  write_csv("clean_data/non_covid_data/ae_wait_times.csv")
---
title: "R Notebook"
output: html_notebook
---
```{r}
library(tidyverse)
library(scales)
library(plotly)
library(lubridate)
library(sf)
library(zoo)
```



## Covid Tab
```{r}
#load in beds and add year column
beds <- read_csv("raw_data/non_covid_raw_data/beds_by_nhs_board_of_treatment_and_specialty.csv") %>% janitor::clean_names()

beds <- beds %>% 
mutate(quarter_date = yq(quarter),
       year = year(quarter_date), 
       .after = quarter)

beds %>% 
write_csv("clean_data/non_covid_data/beds.csv")
```


```{r}
# bed percentage availablity for "all acute"
# Will need to add filter for year based on user input
beds_plotly <- beds %>%
  filter(specialty_name == "All Acute") %>% 
  group_by(quarter, specialty_name) %>%
  summarise(mean_perc_occ = mean(percentage_occupancy)) %>% 
  ggplot(aes(x = quarter, y = mean_perc_occ))+
  geom_line(aes(colour = specialty_name, group = specialty_name))+
  geom_point(aes(
    text = paste0("Occupancy:",round(mean_perc_occ, digits = 2)," %\n", quarter)),
    size = 0.5)+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  labs(title = "Mean bed availability for all Acute Patients",
       x = "\nYear and Quarter",
       y = "Average Percentage Occupancy")

ggplotly(beds_plotly, tooltip = "text") %>% 
  config(displayModeBar = FALSE) %>% 
  layout(hoverlabel=list(bgcolor="white"))


```

## Scotland Shapefile
#### ae_wait_times wrangling
```{r}
ae_wait_times <- read_csv("raw_data/non_covid_raw_data/monthly_ae_waitingtimes_202206.csv") %>% janitor::clean_names()

#make a date and year column with the first date of every month
ae_wait_times <- ae_wait_times %>% 
  mutate(date = ym(month), .after = month,
         year = year(date))

#make a percent column with percent of patients meeting the 4hr target time
ae_wait_times <- ae_wait_times %>% 
  mutate(percent_4hr_target_achieved = (number_meeting_target_aggregate/number_of_attendances_aggregate)*100)

ae_wait_times %>% 
write_csv("clean_data/non_covid_data/ae_wait_times.csv")
```


```{r}
#write in the target data for the shapefile colours
target_2007 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2007) %>% 
  rename(ae_target_2007 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2007)
  

target_2008 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>%
  filter(year == 2008) %>% 
  rename(ae_target_2008 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2008)

target_2009 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2009) %>% 
  rename(ae_target_2009 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2009)

target_2010 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2010) %>% 
  rename(ae_target_2010 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2010)

target_2011 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2011) %>% 
  rename(ae_target_2011 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2011)

target_2012 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2012) %>% 
  rename(ae_target_2012 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2012)

target_2013 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2013) %>% 
  rename(ae_target_2013 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2013)

target_2014 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved =
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2014) %>% 
  rename(ae_target_2014 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2014)

target_2015 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2015) %>% 
  rename(ae_target_2015 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2015)

target_2016 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2016) %>% 
  rename(ae_target_2016 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2016)

target_2017 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2017) %>% 
  rename(ae_target_2017 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2017)

target_2018 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2018) %>% 
  rename(ae_target_2018 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2018)

target_2019 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2019) %>% 
  rename(ae_target_2019 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2019)

target_2020 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2020) %>% 
  rename(ae_target_2020 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2020)

target_2021 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2021) %>% 
  rename(ae_target_2021 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2021)
  
```

#### shape file wrangling
```{r}
scotland <- st_read("../SG_NHS_HealthBoards_2019_shapefile/SG_NHS_HealthBoards_2019.shp")

# make a smaller version for performance issues
scotland_smaller <- scotland %>% 
  st_simplify(TRUE, dTolerance = 2000)
#fixes problems caused by above 
scotland_smaller <- sf::st_cast(scotland_smaller, "MULTIPOLYGON")

#add in the A&E 4 hr target data for each year
scotland_smaller <-  scotland_smaller %>% 
  mutate(centres = st_centroid(st_make_valid(geometry))) %>%
    mutate(lat = st_coordinates(centres)[,1],
           long = st_coordinates(centres)[,2],
           target_2007 = target_2007$ae_target_2007,
           target_2008 = target_2008$ae_target_2008,
           target_2009 = target_2009$ae_target_2009,
           target_2010 = target_2010$ae_target_2010,
           target_2011 = target_2011$ae_target_2011,
           target_2012 = target_2012$ae_target_2012,
           target_2013 = target_2013$ae_target_2013,
           target_2014 = target_2014$ae_target_2014,
           target_2015 = target_2015$ae_target_2015,
           target_2016 = target_2016$ae_target_2016,
           target_2017 = target_2017$ae_target_2017,
           target_2018 = target_2018$ae_target_2018,
           target_2019 = target_2019$ae_target_2019,
           target_2020 = target_2020$ae_target_2020,
           target_2021 = target_2021$ae_target_2021
                  )

#scotland_smaller$Shape_Area <- as.character(scotland_smaller$Shape_Area)
st_write(scotland_smaller, "clean_data/shapefile/scotland_smaller.gpkg", append = FALSE) 

## then to re-load data (also using the dplyr library):
scotland_smaller <- st_read("clean_data/shapefile/scotland_smaller.gpkg") 
 # mutate(Shape_Area = as.integer(Shape_Area))



# # This will require filtered by the year selected in the dashboard
# # Can we get the button or dropdown to pass eg "target_2016" to this in 2 places?
#  mydata2 <- st_collection_extract(scotland_smaller, "POLYGON")
# #I was then able to save the new file properly, i.e...
# st_write(mydata2, "clean_data/scotland_smaller.shp", append = FALSE)
# 
# 
# st_write(scotland_smaller, "clean_data/scotland_smaller.pgkg", append = FALSE)
# 
# scotland_smaller <- st_read("clean_data/scotland_smaller.shp")

p <- ggplot(scotland_smaller) + 
  geom_sf(aes(fill = target_2021, 
              text = paste("<b>", HBName, "</b>\n", round(target_2021, digits = 2),"%", sep = ""))) + 
  scale_fill_viridis_c(option = "plasma", name = "4Hr A&E Target %")+
  theme_void()+
  labs(title = "Percent of A&E depts making the 4hr target")

p %>%
  ggplotly(tooltip = "text") %>%
  style(hoverlabel = list(bgcolor = "white"), hoveron = "fill")%>% 
  config(displayModeBar = FALSE)
```

## SIMD graph 

```{r}
simd <- read_csv("raw_data/non_covid_raw_data/inpatient_and_daycase_by_nhs_board_of_treatment_and_simd.csv") %>% janitor::clean_names()

simd <- simd %>% 
mutate(quarter_date = yq(quarter),
       year = year(quarter_date), 
       .after = quarter)

simd %>% 
write_csv("clean_data/non_covid_data/simd.csv")
```

```{r}
# average episodes by SIMD value
# currently unfiltered for health board or admission type etc
simd_plotly <- simd %>% 
  drop_na(simd) %>%
  mutate(simd = as.factor(simd)) %>% # gives each simd a separate colour
  group_by(quarter, simd) %>% 
  summarise(avg_episodes = mean(episodes, na.rm = TRUE)) %>% 
  ggplot(aes(x = quarter, y = avg_episodes, group = simd))+
  geom_line(aes(colour = simd))+
  geom_point(size = 0.5)+
  scale_y_continuous(labels = scales::comma)+
  labs(title = "Average Hospital Episodes by SIMD Deprevation score\n",
       x = "\nYear and Quarter",
       y = "Average Episodes\n")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplotly(simd_plotly) %>% 
  config(displayModeBar = FALSE)
```

## Age Graph

```{r}
age_sex <- read_csv("raw_data/non_covid_raw_data/inpatient_and_daycase_by_nhs_board_of_treatment_age_and_sex.csv") %>% janitor::clean_names()

age_sex <- age_sex %>%
    mutate(quarter_date = yq(quarter),
           year = year(quarter_date),
           .after = quarter)

age_sex %>% 
  write_csv("clean_data/non_covid_data/age_sex.csv")
```

```{r}

# Average number of episode for age groups
# currently unfiltered by department or anything else
age_plotly <- age_sex %>% 
  #filter(min_date < year & year < max_date) %>% 
  group_by(quarter, age) %>% 
  summarise(avg_episodes = mean(episodes, na.rm = TRUE)) %>% 
  ggplot(aes(x = quarter, y = avg_episodes))+
  geom_line(aes(colour = age, group = age))+ 
  geom_point(size = 0.5)+
  labs(title = "Average Hospital Episodes by Age Groups\n",
       x = "\nYear and Quarter",
       y = "Average Episodes\n")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
  
ggplotly(age_plotly) %>% 
  config(displayModeBar = FALSE)
```

## Gender

```{r}
sex_plotly <- age_sex %>% 
  group_by(quarter, sex) %>% 
  summarise(avg_length_of_episode = mean(average_length_of_episode, na.rm = TRUE)) %>% 
  ggplot(aes(x = quarter, y = avg_length_of_episode))+
  geom_line(aes(colour = sex, group = sex))+
  #geom_smooth(aes(colour = sex, group = sex), se = FALSE)+
  geom_point(size = 0.5)+
  labs(title = "Average Hospital Episodes by Gender\n",
       x = "\nYear and Quarter",
       y = "Average Episodes\n")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
  

ggplotly(sex_plotly, hovertemplate = ) %>% 
  config(displayModeBar = FALSE)
```
## A&E Tab
```{r}
#THE A&E WAI TTIMES IS HANDLED ABOVE IN THE SHAPEFILE WRANGLING SECTION AHHHHH IM SHOUTING

# ae_wait_times <- read_csv("raw_data/non_covid_raw_data/monthly_ae_waitingtimes_202206.csv") %>% janitor::clean_names()
# 
# #make a date and year column with the first date of every month
# ae_wait_times <- ae_wait_times %>% 
#   mutate(date = ym(month), .after = month,
#          year = year(date))
# 
# #make a percent column with percent of patients meeting the 4hr target time
# ae_wait_times <- ae_wait_times %>% 
#   mutate(percent_4hr_target_achieved = (number_meeting_target_aggregate/number_of_attendances_aggregate)*100)
# 
# ae_wait_times %>% 
#   write_csv("clean_data/non_covid_data/ae_wait_times.csv")
```

```{r}
library(rgdal)
library(leaflet)
scotland_smaller <-  readOGR("../SG_NHS_HealthBoards_2019_shapefile/",layer = "SG_NHS_HealthBoards_2019")
shapeData <- spTransform(scotland_smaller, CRS("+proj=longlat +ellps=GRS80"))

x = st_read("clean_data/shapefile/scotland_smaller.gpkg")  

scotland <-  scotland %>% 
  mutate(centres = st_centroid(st_make_valid(geometry))) %>%
    mutate(lat = st_coordinates(centres)[,1],
           long = st_coordinates(centres)[,2])


scotland <- st_transform(x, '+proj=longlat +datum=WGS84')

pal <- colorNumeric("viridis", NULL)

m <- leaflet() 
m %>% addTiles() %>% 
  addPolygons(data=scotland,
              smoothFactor = 0.3, 
              fillOpacity = 1,
              fillColor = ~pal(target_2007),
              label = ~paste0( HBName,": ", target_2007),
              weight = 1, 
              highlightOptions = highlightOptions(color = "white", 
                                                            weight = 2, 
                                                            bringToFront = TRUE)) %>% 
  addLegend(pal = pal, values = scotland$target_2007, opacity = 1, )
```

